Raising the bar on the evaluation of out-of-distribution detection
In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datasets, arbitrarily different from training data. There is no clear definition of what forms a "good" OoD dataset...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
EEE
2023
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